Tensorflow 简明教程

TensorFlow - Gradient Descent Optimization

梯度下降优化被认为是数据科学中的一个重要概念。

Gradient descent optimization is considered to be an important concept in data science.

考虑下面所示的步骤来了解梯度下降优化的实现 −

Consider the steps shown below to understand the implementation of gradient descent optimization −

Step 1

包含必需的模块并声明 x 和 y 变量，我们将通过它们来定义梯度下降优化。

Include necessary modules and declaration of x and y variables through which we are going to define the gradient descent optimization.

import tensorflow as tf

x = tf.Variable(2, name = 'x', dtype = tf.float32)
log_x = tf.log(x)
log_x_squared = tf.square(log_x)

optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(log_x_squared)

Step 2

初始化必要的变量，并调用优化器为其定义并使用各自函数调用它。

Initialize the necessary variables and call the optimizers for defining and calling it with respective function.

init = tf.initialize_all_variables()

def optimize():
   with tf.Session() as session:
      session.run(init)
      print("starting at", "x:", session.run(x), "log(x)^2:", session.run(log_x_squared))

      for step in range(10):
         session.run(train)
         print("step", step, "x:", session.run(x), "log(x)^2:", session.run(log_x_squared))
optimize()

上面这一行代码生成的输出如以下截图所示 −

The above line of code generates an output as shown in the screenshot below −

initialize variables

我们可以看到必要的 epoch 和迭代如输出所示已经计算出来。

We can see that the necessary epochs and iterations are calculated as shown in the output.